Design and development method of multi-mode high-simulation training integrated platform

Abstract

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a design and development method of a multi-mode high-simulation training integrated platform, which constructs a highly realistic virtual substation environment by combining a virtual reality technology and multi-sensor simulation, and is used for unmanned aerial vehicle inspection training and operator skill improvement. The platform comprises a scene simulation core module, an environment simulation core module, an unmanned aerial vehicle sensor simulation core module and an unmanned aerial vehicle dynamics simulation core module, real perception and reaction data are provided, and the actual flight behavior and performance of the unmanned aerial vehicle are accurately reflected. Besides, the platform is also integrated with an immersive training system of the operator, and comprehensive evaluation and improvement of the performance of the operator are realized through multi-scene simulation flight training, an intelligent scoring system and a high-simulation visualization function. According to the invention, a reliable tool is provided for the development and test of the unmanned aerial vehicle, and a safe and efficient solution is provided for the training and evaluation of operators.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle inspection technology, and in particular to a design and development method of a multi-modal high-simulation operation and training integrated platform. Background Art

[0002] With the development of the information age, power grid, as the cornerstone of the times, has become increasingly important in all aspects of people's daily lives. Substations are the core of the power grid system, and their safe and stable operation is the basis for the normal operation of the entire power system.

[0003] In traditional unmanned inspection and maintenance of substations, most of the equipment used is ground robots. Although ground inspection robots have autonomy, their inspection perspective is mainly on the ground and low altitude. They cannot effectively inspect equipment and instruments in the sky. There are blind spots in the field of vision, and they cannot complete the full coverage inspection of the substation area. At the same time, ground robots cannot pass through some places, so they cannot grasp all the defects and hidden dangers of the line in time.

[0004] Because drone inspection not only has good three-dimensional space activity ability and low-altitude all-round perspective, but also can obtain high-quality and detailed inspection information from multiple perspectives, achieving all-round coverage and detailed inspection of the inspection area. Therefore, people now begin to use drones to cooperate with ground robots in substations or directly replace ground robots for inspection operations. However, before drones are deployed to substations for automatic inspection, a lot of debugging work is required to achieve good application effects. The debugging work is extremely risky and cannot be completed in the real environment of substations with extremely high safety requirements. At the same time, due to the environmental complexity and high safety requirements of substations, the risks and training costs of manual operations are too high. Therefore, it is necessary to develop corresponding drone inspection training simulators. However, there is also a problem that the existing drone training simulators lack the perception dimension compared with the real substation environment, and there is a huge gap when operators transfer skills from simulators to the real environment.

[0005] In response to the above problems, the multi-modal high-simulation UAV precision inspection and training integrated platform to be developed by the present invention uses virtual reality simulation technology to create a virtual substation environment, and conducts training based on the operation parts of the simulator and the virtual environment. The simulated driving environment almost completely clones the real substation environment, which can eliminate the operator's fear and standardize the operator's operation in a timely manner. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a multi-modal high-simulation training integrated platform design and development method, which provides a highly realistic simulation environment and immersive training experience by combining virtual reality technology and multi-sensor simulation of the UAV simulation and training system, thereby improving the skill level and task execution ability of the UAV operator. The technical solution of the present invention mainly consists of the following two parts:

[0007] (1) Design of UAV simulator and human-computer interaction simulator:

[0008] The first part of the present invention includes the design of a UAV simulator and the design of a human-computer interaction simulator.

[0009] Design of the UAV simulator. First, to ensure the rapidity of the simulator, the present invention introduces a multi-modal modular design concept into the overall design. Second, to ensure the physical and visual authenticity, the UAV simulator of the present invention includes four modules: scene simulation module, environment simulation module, UAV sensor simulation module and UAV dynamics simulation module.

[0010] The scenario simulation module is achieved by conducting on-site surveys of substations and using structured vector graphics of high-precision maps for 3D rendering to create an accurate three-dimensional model.

[0011] The environmental simulation module covers changes in environmental factors such as wind, temperature, and light, and considers the impact of these factors on UAV performance in the simulation.

[0012] The drone sensor simulation module provides perception and response data in real environments by simulating the functions and outputs of multiple sensors such as inertial measurement units, optical sensors, and distance measurement sensors.

[0013] The UAV dynamics simulation module describes in detail the rigid body dynamics, component models, flight dynamics and control systems of the UAV, and establishes the aerodynamic equations and dynamic models of the UAV to ensure that the simulator can accurately reflect the motion behavior and performance of the UAV in actual flight.

[0014] Design of human-computer interaction simulator. The human-computer interaction simulator is an important innovation of this invention, which aims to provide drone operators with a more realistic operating experience. The human-computer interaction simulator integrates a variety of human-computer interaction technologies such as tactile feedback, voice interaction, and virtual remote control, allowing operators to experience operation feedback and interaction methods that are highly consistent with real operations in a virtual environment. This not only improves the immersion of training, but also helps operators quickly adapt and respond correctly when faced with complex operations.

[0015] These components together form a complete UAV simulation system, providing a reliable tool for UAV development and testing.

[0016] (2) Immersive training for drone operators

[0017] In the second part of the invention content, the main purpose is to ensure an immersive training environment for operators. This part consists of three parts: an operator attention detection and evaluation module, an operator performance intelligent scoring module, and a high-simulation visualization assessment and supervision module.

[0018] First, in the operator attention detection and evaluation module, the operator's visual attention is trained by equipping the operator with eye tracking glasses and a remote control to conduct multi-scenario, multi-task simulated flight training, so that the operator's flight experience is as close to the real world as possible. The operator needs to improve his operating proficiency during the training process and complete the task without collision. Eye tracking glasses are used to capture the operator's visual data and record raw gaze data and calibration data at a specific frequency. The calibration and recalibration process ensures the accuracy of the data and maintains the stability of the equipment during the experiment to prevent errors caused by small displacements.

[0019] Secondly, the operator performance intelligent scoring module evaluates the operator's performance through four main indicators: smoothness cost, energy cost, time cost and collision cost. Smoothness cost measures the smoothness of the trajectory during flight, energy cost evaluates the energy consumption in task execution, time cost measures the speed of task completion, and collision cost focuses on flight safety. By combining these indicators, the operator's performance can be accurately evaluated and targeted training can be carried out to improve operating skills and task execution efficiency.

[0020] Finally, the high-simulation visualization assessment and supervision module realizes the combination of high-simulation visualization, assessment and supervision functions, providing users with a comprehensive training and evaluation platform. Through high-simulation technology, the platform can create a realistic drone flight environment and show various scenarios in real inspection tasks. The visualization function provides an intuitive flight path and data display. The assessment and supervision function monitors and evaluates the quality and compliance of task execution in real time to ensure that operators can follow regulations and complete tasks safely and efficiently. This system not only improves the skill level of operators, but also provides managers with effective task management tools.

[0021] Compared with the prior art, the beneficial technical effects of the present invention are:

[0022] ① Multimodal modular design: This invention introduces the concept of multimodal modular design, and divides the UAV simulation system into four modules: scene simulation, environment simulation, UAV sensor simulation, and dynamics simulation. This design not only improves the flexibility and scalability of the system, but also can more accurately simulate the performance of UAVs in different environments.

[0023] ② Highly realistic environmental simulation: Through high-precision maps and simulation of multiple environmental factors, such as wind, temperature and light changes, the present invention provides a flight training platform that is closer to the real environment, thereby improving the operator's actual combat capability.

[0024] ③ Comprehensive human-computer interaction simulation: The platform integrates multiple human-computer interaction technologies such as tactile feedback, voice interaction and virtual remote control, enabling operators to obtain an experience in a virtual environment that is highly consistent with actual operations, enhancing the immersion of training and the realism of operation feedback.

[0025] ④ Intelligent training evaluation system: Through eye tracking equipment and intelligent scoring system, the platform can comprehensively evaluate the operator's performance, covering indicators such as smoothness, energy consumption, time efficiency and safety. This system can accurately detect and feedback the operator's skill level and provide targeted improvement suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of a platform design and development method provided according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of the overall structure of a drone simulator provided according to an embodiment of the present invention;

[0028] Figure 3 is a schematic diagram of a drone sensor provided according to an embodiment of the present invention;

[0029] Figure 4 is a force and moment diagram acting on a single propeller provided according to an embodiment of the present invention;

[0030] Figure 5 is a timeline diagram of an operator training procedure provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] A preferred embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0032] like Figure 1 As shown, the specific process of the present invention is as follows Figure 1 As shown, the following steps are included:

[0033] S1. Design of UAV simulator and human-computer interaction simulator;

[0034] S2. Immersive training for drone operators.

[0035] 1. Design of UAV simulator and human-computer interaction simulator

[0036] 1.1 Design of UAV simulator

[0037] like Figure 2 As shown, the UAV simulator design of this part mainly consists of four parts: scene simulation, environment simulation, UAV sensor simulation, and UAV dynamics simulation.

[0038] (1) Scenario simulation

[0039] By inspecting the substation on site and taking photos, we can build an accurate 3D model. The scene reconstruction technology based on the HD map can render the structured vector graphics of the HD map in 3D again. The method is to establish a one-to-one standard model material library for each vector graphic of the HD map, and then call different resources in the material library to re-render according to the semantics of the vector graphics, so as to complete the establishment of the substation scene.

[0040] (2) Environmental simulation

[0041] The simulation of the environment includes the simulation of various environmental factors:

[0042] Wind simulation: Wind is one of the important environmental factors in UAV flight. Wind simulation includes information such as wind direction, wind speed and changes in wind field. By introducing a wind field model into the simulator, the flight behavior of the UAV under different wind directions and wind speeds and its impact on the UAV are simulated.

[0043] Temperature simulation: Temperature has a significant impact on drone performance and battery life. Temperature simulation can simulate different temperature conditions and consider the impact of temperature on electronic components, sensors, and batteries to more accurately evaluate drone performance and flight time.

[0044] Lighting simulation: Lighting conditions are critical to the perception capabilities of visual sensors and cameras. Lighting simulation can simulate changes in lighting at different times and weather conditions to evaluate the perception and navigation capabilities of drones under different lighting conditions.

[0045] In addition to the above factors, other environmental factors can also be considered, such as humidity, atmospheric pressure, etc. By simulating environmental factors in a drone simulator, developers can better understand the behavior of drones under different environmental conditions, test the performance and robustness of drones, and optimize and verify algorithms and control systems.

[0046] (3) UAV sensor simulation

[0047] Drone sensor simulation is the process of simulating and emulating the functions and outputs of various sensors used by drones in a drone simulator. Drones use various sensors to sense their surroundings and obtain critical flight information. Common drone sensors include:

[0048] Inertial measurement unit: includes accelerometers and gyroscopes, which are used to measure the linear acceleration and angular velocity of the drone to determine the drone's attitude and motion state.

[0049] Optical sensors: including cameras and cameras, used to acquire images, videos and visual information for tasks such as target recognition, map construction, and obstacle avoidance.

[0050] Distance measurement sensors: such as ultrasonic sensors, lidar, and infrared sensors, which are used to measure the distance between the drone and surrounding objects and for obstacle detection.

[0051] Global Positioning System: Used to determine the drone's location and navigation information.

[0052] Barometric pressure sensor: used to measure atmospheric pressure and thus estimate the altitude and height of the drone.

[0053] Magnetometer: Used to measure the Earth's magnetic field to assist in the navigation and orientation of drones.

[0054] Temperature and humidity sensor: used to measure the temperature and humidity of the surrounding environment.

[0055] Sonar Sensor: Used for sound navigation and ranging in water.

[0056] In a drone simulator, by simulating the working principles and outputs of these sensors, the perception and reaction of the drone in the real environment can be simulated. This allows developers to test and verify the perception algorithms, navigation algorithms, and control systems of the drone without actual flight conditions. Sensor simulation can provide real-world data and simulate the noise, error, and uncertainty of real sensors. This is very valuable for the development, testing, and training of drone applications.

[0057] (4) UAV dynamics simulation

[0058] UAV dynamics modeling describes the motion behavior of UAVs. A mathematical model is established based on the physical characteristics of the UAV and environmental parameters to predict and control the motion behavior of the UAV.

[0059] In the UAV dynamics modeling, the following aspects are generally included: 1) Rigid body dynamics: consider the motion behavior of the UAV as a rigid body, including position, attitude, linear velocity and angular velocity, etc. 2) Parts model: consider the components of the UAV, such as the physical properties of the body, rotor, motor, etc., and the interactions between them. 3) Flight dynamics: describe the dynamic characteristics of the UAV in different flight stages (such as take-off, flight, landing, etc.), such as lift, drag, thrust, etc. 4) Control system: establish the control algorithm and controller of the UAV to achieve the desired motion behavior. 5) External environment: consider the external disturbances of the UAV under different environmental conditions, such as wind speed, air pressure, etc.

[0060] When conducting aerodynamic modeling of the underlying blades of a drone, factors such as blade geometry, material properties, and rotation speed need to be considered, and aerodynamic theory and experimental data need to be combined for analysis and calculation. Effective blade aerodynamic modeling can help better understand the flight performance and control characteristics of drones, thereby designing optimized blades and improving flight efficiency.

[0061] The power of a multi-rotor drone comes entirely from the propellers. The force on a single propeller is as follows: Figure 4 As shown. Let F T 、F D 、M R 、M D They represent the thrust, air resistance, rolling moment and the moment from blade resistance. The aerodynamic equation of the drone is as follows:

[0062] F T =ω 2 C T ·e zB ;

[0063]

[0064] M D =-∈C M ·F T ;

[0065] Among them, ω, C T , C D , C R , C M Respectively represent the blade angular velocity, thrust coefficient, drag coefficient, rolling moment coefficient, blade moment coefficient, all of which are positive numbers. ∈ represents the rotor direction, +1 is counterclockwise, -1 is clockwise. e zB is a unit vector pointing in the positive direction of the z-axis of its body coordinate system. Represents vector v A Projection on the rotor plane.

[0066] 1.2 Human-computer interaction simulator

[0067] The human-machine interaction simulator is designed to provide a realistic operating experience, allowing operators to feel feedback that is highly consistent with actual operations in a virtual environment. The simulator integrates a variety of interactive technologies, including tactile feedback, voice interaction, virtual remote control, etc. The tactile feedback device can simulate the operator's feel and reaction force when controlling the drone, making it closer to the real operating experience. The voice interaction system uses natural language processing technology to achieve voice communication between the operator and the simulation system and enhance interactivity. The virtual remote control can simulate the remote control device in reality to help the operator become familiar with the actual operation in a virtual environment.

[0068] The human-machine interaction simulator also supports multiple scene switching and task settings. Operators can train in different environments to improve their adaptability and operation level. In addition, this module is combined with drone sensor simulation and dynamics simulation to provide real-time feedback on the operator's instructions and the drone's response, further enhancing the realism of the simulation. The introduction of this part significantly improves the interactivity and immersion of the drone simulator, providing operators with a more realistic and effective training platform.

[0069] 2. Immersive training for drone operators

[0070] In the immersive training of drone operators on the multi-modal high-simulation drone precision inspection and training integrated platform, the platform records the operator's visual attention through eye tracking equipment, and intelligently evaluates performance based on indicators such as smoothness, energy, time and collision cost, providing feedback to improve skills. The high-simulation visualization and assessment functions simulate real inspection tasks, provide practical operation experience and monitoring methods, and ensure the safety and compliance of tasks. This integrated system not only improves the quality of operator training, but also provides managers with effective monitoring tools.

[0071] (1) Operator visual attention

[0072] To collect the operator's visual attention, the operator was equipped with eye-tracking glasses and a remote control, and the eye tracker was calibrated. The operator conducted training flights and separate flight maneuver tests on different tracks that were not used for experiments, and participated in training in different preset tasks in different training scenarios. The goal is to make the flight experience as close to real-world drone flight as possible. The operator repeatedly operated the drone on the same track and increased the single lap speed over time. The operator should complete as many laps as possible in the shortest possible time, and fly as fast as possible without collision. If the operator's drone collides with an obstacle, the flight training is terminated early.

[0073] The operator wore a pair of glasses eye-tracking devices to record eye and world videos at a certain Hz frequency. Pupil capture software was used to capture raw gaze data and eye calibration data and save them to the hard drive of the eye-tracking laptop. Monocular tracking was used in this method because dominant eye movements are considered more accurate than non-dominant eye movements. Since the operator viewed the computer monitor at a fixed distance, information from stereo tracking was not required. Eye tracker calibration was performed at the beginning of the training and recalibrated every 15 minutes during the drone competition training recording. The calibration process consisted of two repetitions of the pupil capture software built-in calibration procedure. On a gray background, a fixed target (i.e., a black dot pattern) was displayed alternately at 12 target locations on the screen for 2 seconds with a 1-second inter-stimulus interval. The second calibration run was used to verify the calibration quality of the first run, and the median verification accuracy of the calibration runs for all subjects was 0.5 degrees of visual angle. During the training, the operator kept his head in a fixed position, avoided touching his face, and kept his facial muscles relaxed to prevent the calibration quality from being degraded due to small displacements of the eye tracker.

[0074] (2) Intelligent scoring of operator performance

[0075] Scoring drone operators, correcting shortcomings when they are discovered, and helping them quickly improve their operating skills. Intelligent scoring of operator performance is mainly based on the following four cost functions:

[0076] ① Smoothness cost: The design of the smoothness of the drone trajectory is considered in the integrated platform for multi-modal high-simulation drone precision inspection and training. The smoothness cost is an indicator for evaluating the smoothness of the operator's actions or the operation of the drone. It can be used to measure the degree of change or curvature of the drone's flight trajectory or track segment, thereby evaluating its smoothness. By analyzing the acceleration changes of the drone and calculating the integral of the sum of the squares of the acceleration, the operator's skill level and operating efficiency can be effectively evaluated. The smaller the value of the smoothness cost, the smoother and more fluent the position and speed changes of the drone during flight. Conversely, the larger the value, the less smooth the position and speed changes during flight. The smoothness cost formula is as follows:

[0077]

[0078] Where x(t) represents the position of the UAV at time t.

[0079] ②Energy cost: Energy cost is an important evaluation indicator in the integrated platform of multi-modal high-simulation UAV precision inspection and training, which is used to measure the energy consumed by the UAV when performing tasks. By incorporating the flight status, environmental conditions and mission requirements of the UAV into the energy consumption model, the energy consumption during a specific flight can be calculated. By designing appropriate energy cost functions and optimization algorithms, the energy consumption of the UAV can be reduced, flight efficiency can be improved, and endurance time can be extended, providing operators with better operating experience and training effects. The energy cost calculation formula is as follows:

[0080] C energy =∫P(t)dt;

[0081] Where P(t) represents the power consumption of the drone at time t.

[0082] ③Time cost: It is used to measure the time spent by the drone in performing the mission. Time efficiency is crucial for drone missions because it is directly related to the efficiency and punctuality of mission execution. The time cost calculation formula is as follows:

[0083] C rime =T total ;

[0084] ④ Collision cost: Collision cost refers to the risk and potential loss of a drone colliding with the surrounding environment or objects during a drone inspection mission. Considering the collision cost can help adjust the flight path and speed of the drone in real time to avoid collisions with obstacles. Ensure that the drone maintains a safe distance during the inspection process and minimize the risk of collision. The collision cost calculation formula is as follows:

[0085]

[0086] where d i Represents the distance between the drone and the i-th obstacle.

[0087] During the design process, the above cost functions can be weighted to comprehensively consider the impact of multiple factors. The choice of weights can be adjusted according to specific task requirements and operator priorities. By intelligently scoring operator performance, the performance of different operators can be evaluated and compared, and the cost function can be fine-tuned to improve operator performance and training results. The weighted cost is shown below:

[0088] C total =w smooth C smooth +w energy C energy +w time C time +w collision C collision .

[0089] The w here is the weight value given according to the actual situation.

[0090] (3) High-simulation visualization, assessment and supervision

[0091] Through the combination of high-simulation visualization and assessment and supervision functions, users can conduct practical training and inspection simulations on the platform, while monitoring and evaluating the details and indicators of task execution in real time. This will provide a comprehensive training and evaluation method to help users improve their inspection skills and execution capabilities. In addition, regulatory authorities and managers can also use the platform's monitoring and analysis functions to effectively manage and supervise drone inspection tasks. The following introduces each of them.

[0092] High Simulation: The platform will use advanced simulation technology to create a highly realistic drone flight environment. Through carefully designed scenes and details, various situations in real inspection tasks are simulated to provide users with an immersive experience. The appearance, flight dynamics, sensor data, etc. of the drone will be accurately simulated, allowing users to experience the visualization effect of real inspection tasks.

[0093] Visualization: The platform will use graphics processing and data visualization technology to present the drone's flight and inspection process to users in an intuitive way. Through realistic scene rendering, real-time data display and interface design, users can clearly observe the drone's flight path, sensor data, inspection results and other key information, so as to better understand the execution of the task.

[0094] Assessment and supervision: The platform will provide a series of assessment and supervision functions to evaluate and monitor the execution quality and compliance of drone inspection tasks. Users can evaluate and analyze the flight trajectory, data collection quality, task completion, etc. of drones according to preset standards and indicators. At the same time, the system can also monitor the flight status, sensor data, and compliance regulations and measures of drones in real time to ensure the safety and compliance of tasks.

[0095] In summary, the multi-modal high-simulation training integrated platform of the present invention has the following advantages over the prior art:

[0096] High-fidelity virtual environment: By combining virtual reality technology and multi-sensor simulation, a highly realistic virtual substation environment is created, which can more realistically simulate actual inspection scenarios.

[0097] Comprehensive simulation modules: The platform includes four core modules: scenario simulation, environment simulation, UAV sensor simulation and UAV dynamics simulation, which comprehensively cover all aspects of UAV flight and operation, and significantly improve the authenticity and reliability of the simulation system.

[0098] Immersive training system: Integrates operator visual attention detection, intelligent scoring system and high-simulation visualization functions to provide operators with a comprehensive and immersive training experience, filling the missing perceptual dimension and skill transfer gap in traditional training methods.

[0099] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0100] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A design and development method for a multi-modal high-simulation training integrated platform, characterized in that: Specifically include: (1) Design of UAV simulator and human-computer interaction simulator: UAV simulator design: The UAV simulator includes scene simulation module, environment simulation module, UAV sensor simulation module and UAV dynamics simulation module; The scenario simulation is carried out by surveying the substation on site and using the structured vector graphics of the map for 3D rendering to establish an accurate 3D model; Environmental simulation module, used to simulate the impact of changes in wind, temperature, and light on UAV performance in simulation; UAV sensor simulation module, used to simulate the functions and outputs of inertial measurement units, optical sensors, and distance measurement sensors; UAV dynamics simulation module, used to simulate the rigid body dynamics, parts model, flight dynamics and control system of the UAV, establish the aerodynamic equations and aerodynamic model of the UAV, so that the simulator can reflect the motion behavior and performance of the UAV in actual flight; Design of human-computer interaction simulator: The human-computer interaction simulator module integrates tactile feedback, voice interaction, and virtual remote control, allowing operators to experience operational feedback and interaction methods consistent with real operations in a virtual environment; (2) Immersive training for drone operators: This is achieved through the operator attention detection and evaluation module, the operator performance intelligent scoring module, and the assessment and supervision module; The operator attention detection and evaluation module conducts multi-scenario, multi-task simulated flight training for the operator through the equipped eye-tracking glasses and remote control. The eye-tracking glasses are used to capture the operator's visual data and record the original gaze data and calibration data at a specific frequency. The operator performance intelligent scoring module evaluates the operator's performance through the following four indicators: smoothness cost, energy cost, time cost and collision cost; smoothness cost measures the smoothness of the trajectory during flight, energy cost evaluates the energy consumption in task execution, time cost measures the speed of task completion, and collision cost focuses on flight safety; by combining these indicators, the operator's performance is accurately evaluated and targeted training is carried out to improve operating skills and task execution efficiency; The assessment and supervision module uses high-simulation technology to create a drone flight environment and display various scenarios in real inspection tasks. It uses visualization functions to display flight paths and data. The assessment and supervision functions allow for real-time monitoring and evaluation of mission execution quality and compliance.

2. The method for designing and developing a multi-modal high-simulation training integrated platform according to claim 1 is characterized in that: The aerodynamic equations and aerodynamic models of the UAV are established, specifically including: Let F T 、F D 、M R 、M D They represent the thrust, air resistance, rolling moment and the moment from blade resistance on the drone respectively; the aerodynamic equation of the drone is as follows: F T =ω 2 C T ·e zB ; M D =-∈C M ·F T ; Among them, ω, C T , C D , C R , C M They represent blade angular velocity, thrust coefficient, drag coefficient, rolling moment coefficient, and blade moment coefficient, respectively, all of which are positive numbers; ∈ represents the direction of the rotor, +1 is counterclockwise, -1 is clockwise; e zB is a unit vector pointing to the positive z-axis of the body coordinate system; Represents vector v A Projection on the rotor plane.

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